Questions Business Leaders Should Ask Before Approving an AI Initiative

This post is part of the AI in Production series, a five-part examination of what it takes to deploy AI successfully inside complex operational environments. The series is written for both business and technical leaders, with content that speaks to both where they converge and where their priorities diverge. Post 4 of 5.

The previous posts in this series established where AI breaks in production, which workflows carry the most financial exposure, and what re-engineering requires at the organizational and architectural level. This post is a practical tool for business leaders: a set of questions to ask when evaluating any AI initiative, whether proposed by an internal team, a vendor, or an existing deployment under review. If you’re a technical leader, we wrote a version for you here.

Strong answers to these questions indicate an initiative built for production. Weak answers, or the absence of answers, indicate an initiative built for a demo.

Accountability and measurement

Which department will fund the initiative?

Unclear funding ownership signals unclear outcome ownership. When no single department holds the budget, accountability diffuses, and so does urgency. Projects without clear financial sponsors tend to drift rather than deliver.

What KPIs will move in the first 90 days?

Without specific, time-bound targets, success becomes subjective. Teams default to measuring activity (adoption rates, tickets closed) rather than outcomes (cost per unit, error reduction, throughput improvement). The KPIs worth tracking are the ones that connect directly to financial performance: completion times, cost per unit, error rate, throughput.

How will those KPIs be measured?

Agreeing on what to measure is insufficient without the instrumentation to measure it. If the data infrastructure to track target KPIs doesn't exist, building it must be treated as part of the initiative scope, not an afterthought.

Where do KPIs stand today?

No baseline means no measurable ROI. Organizations frequently underestimate how difficult it is to reconstruct historical performance after a new system is in place. Establishing the operational baseline before deployment is a prerequisite for demonstrating that the investment produced results.

Workflow and authority

What workflows will change, not just what tools will be added?

Adding new technology without changing workflows rarely moves outcome-specific KPIs. If the answer is that a tool is being added but processes remain the same, the initiative is unlikely to deliver the financial results it promises.

Who has decision authority for the affected workflows?

AI deployments that touch workflows spanning multiple owners require either clear authority or genuine consensus. Without one or the other, every decision point becomes a negotiation, and compromises accumulate in ways that degrade the system's ability to execute.

How will decisions move from approved to operational?

The gap between approval and production is where many initiatives stall. Seasonality, change freezes, training dependencies, and downstream system impacts all introduce delays that compound. A phased rollout planned around these realities performs more reliably than one that treats them as edge cases.

Control and risk

Who is responsible for ongoing performance monitoring?

Launch is not the finish line. Systems degrade. Models drift. Data quality erodes. Workarounds accumulate. Without assigned ownership for ongoing performance monitoring, efficiency gains erode quietly and invisibly.

How does the system prevent automation from amplifying errors?

Automation amplifies whatever it touches, including errors. A manual process might produce one bad invoice. An automated one might produce thousands before anyone notices. The initiative should define checkpoints, thresholds, and intervention mechanisms before deployment, not after a problem surfaces.

How are exceptions handled so they don't overwhelm teams?

Poorly designed exception handling creates a paradox: automation reduces routine work while flooding teams with edge cases that lack the context needed for resolution. Planning for exception volume, categorization, and routing is as important as planning for the routine case.

What is the governance and auditability approach for AI-recommended actions?

AI-recommended actions that can't be explained create compliance risk and erode operator trust. The level of traceability required by regulators, auditors, and internal stakeholders should be defined before the system is built, not retrofitted after it's in production.

What failure modes is the system designed for?

Bad inputs, latency spikes, model drift, partial outages; Each failure type requires a planned response. Systems designed with explicit degradation paths fail gracefully. Systems that treat failure as an edge case fail catastrophically.

Adoption and sustainability

What does the initiative require of operators and business owners?

Every implementation carries change burdens: time spent learning, process adjustments, new responsibilities. Underestimating these requirements produces resistance and workarounds that limit the system's effectiveness regardless of how well it's engineered.

How does adoption show up in the way people work?

Logins and clicks measure activity. The real indicator is whether the system has become integral to how work gets done. If users can (and do) easily bypass it, the organization has built an optional tool rather than operational infrastructure.

How is delivery phased so operations don't stall during the project?

Big-bang deployments carry big-bang risk. Phased delivery reduces exposure but requires careful sequencing, since some capabilities depend on others and some workflows can't be partially automated. Planning the sequence before committing to a timeline is essential.

How does value compound after phase one?

One-off builds deliver one-off value. Reusable models, extensible workflows, and documented patterns allow phase one investments to accelerate phase two. Without intentional design for reuse, each subsequent deployment starts from scratch.

How will knowledge transfer from vendors to internal teams be managed?

Vendor expertise that exits at project close represents a slow-motion failure. What internal teams need to own, operate, and extend the system should be defined before the engagement begins, then built into the delivery plan, not appended to it.

How to use these questions

These questions serve as a filter, not a checklist. A strong AI initiative doesn't need a perfect answer to every one of them, but it should have clear, specific answers to most. Vague responses, deferred accountability, and undefined baselines are consistent indicators of initiatives that will struggle in production.

The goal is to distinguish between AI that is built to perform under the real conditions of a live operational environment and AI that is built to look compelling under the controlled conditions of a demo.

The final post in this series gives technical leaders a parallel set of questions focused on execution architecture, data integrity, observability, governance, and operational lifecycle. Read it here: Technical Due Diligence for AI in Production